System and method for forecasting loyalty program liability

Abstract

In a system and method for providing a points liability forecast, data associated with transactions related to a retail loyalty program based on points accumulated by each customer enrolled in the retail loyalty program is received and stored. One or more training sets of data is created based on the received and stored data. The one or more training sets are used to generate a machine-learning model that forecasts points liability. Input parameters related to retail loyalty program are received from aa user, for input to the machine learning model. Forecast parameters based on the input parameters are received, as output from the machine learning model. Finally, the forecast parameters are provided to the user via an interface.

1 . A method for forecasting retail loyalty program financial liabilities, comprising:
 receiving, from one or more point-of-sale (POS) systems, retail transaction records comprising purchased item identifiers, quantities, prices, and associated loyalty program point events;   receiving, from one or more customer service systems, adjustment records associated with loyalty point issuance, returns, expirations, bonuses, and manual overrides;   creating one or more training sets of data that each combines the POS transaction records with the adjustment records, wherein each training set of data includes a plurality of structured and unstructured data fields representative of multi-dimensional customer loyalty interactions;   training, by one or more processors, a machine-learning model using the one or more training sets, machine-learning model being configured to capture temporal and non-linear relationships between retail activity and loyalty point liability;   receiving from a user, for input to the machine learning model, input parameters related to the retail loyalty program and a defined forecast scenario;   receiving, as output from the machine learning model, forecast parameters based on the input parameters that provide a forecasted loyalty point liability balance for a defined future period; and   providing the forecast parameters to the user;   wherein the one or more training sets of data includes at least two distinct data types selected from: (i) item-level POS transaction data, (ii) customer profile and segment data, (iii) promotional campaign metadata, (iv) point expiration schedules, and (v) customer service adjustment records.   
     
     
         2 . (canceled) 
     
     
         3 . The method of  claim 1 , wherein the the adjustment records comprise customer-service-initiated loyalty point corrections that are not associated with a retail purchase transaction. 
     
     
         4 . The method of  claim 1 , wherein the retail transaction records comprise all loyalty members data as captured in consumer data management records and/or all promotions data that involve points. 
     
     
         5 . The method of  claim 1 , wherein the retail transaction records comprise points expiration data for each enrolled loyalty member. 
     
     
         6 . The method of  claim 1 , wherein the input parameters related to the retail loyalty program comprise the loyalty program for which a forecast is requested, the loyalty customer segment to be included in the forecast, and/or the forecast period. 
     
     
         7 . The method of  claim 1 , wherein the forecast parameters comprise a forecast of loyalty points to be gained by customers during a defined forecast. 
     
     
         8 . The method of  claim 1 , wherein the forecast parameters comprise a forecast of loyalty points to be redeemed by customers during the defined forecast period during retail purchase transactions at point-of-sale systems. 
     
     
         9 . The method of  claim 1 , wherein the forecast parameters comprise a forecast of loyalty points adjustments to be made during the defined forecast period initiated by retailer personnel. 
     
     
         10 . The method of  claim 1 , wherein the forecast parameters comprise a forecast of the retailer's overall points liability at the end of the defined forecast period. 
     
     
         11 . A system for forecasting retail loyalty program liabilities, comprising:
 a retail location server comprising at least one processor and an associated non-transitory computer-readable storage medium, the retail location server being coupled to one or more point-of-sale (POS) systems;   a remote server comprising at least one processor and an associated non-transitory computer-readable storage medium, the remote server coupled to the retail location server;   the non-transitory computer-readable storage medium associated with the remote server comprising executable instructions; and   the executable instructions when executed by at least one processor in the remote server cause the at least one processor to perform operations, comprising:   receiving, from the one or more POS systems via the retail location server, retail transaction records comprising purchased item identifiers, quantities, prices, and associated loyalty program point events;   receiving, from one or more customer service systems, adjustment records associated with loyalty point issuance, returns, expirations, bonuses, and manual overrides;   creating one or more training sets of data that each combine the POS transaction records with the adjustment records, wherein each training set of data includes a plurality of structured and unstructured data fields representative of multi-dimensional customer loyalty interactions;   training a machine-learning model using the one or more training sets, the machine-learning model being configured to capture temporal and non-linear relationships between retail activity and loyalty point liability;   receiving from a user, for input to the machine learning model, input parameters related to the retail loyalty program and a defined forecast scenario;   receiving, as output from the machine learning model, forecast parameters based on the input parameters that provide a forecasted loyalty point liability balance for a defined future period; and   providing the forecast parameters to the user;   wherein the one or more training sets of data include at least two distinct data types selected from: (i) item-level POS transaction data, (ii) customer profile and segment data, (iii) promotional campaign metadata, (iv) point expiration schedules, and (v) customer service adjustment records.   
     
     
         12 . The system of  claim 11 , comprising a business office computer comprising at least one processor and an associated non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium associated with the business office computer comprising executable instructions and the executable instructions when executed by at least one processor in the business office computer cause the at least one processor to perform operations, comprising:
 forward input parameters related to the retail loyalty program and the defined forecast scenario entered by the user to the remote server;   receive the forecast parameters based on the input parameters from the remote server; and   display the received forecast parameters on a user interface associated with the business office computer.   
     
     
         13 . The system of  claim 11 , wherein the the adjustment records comprise customer-service-initiated loyalty point corrections that are not associated with a retail purchase transaction. 
     
     
         14 . The system of  claim 11 , wherein the retail transaction records comprise all loyalty members data as captured in consumer data management records and/or all promotions data that involve points. 
     
     
         15 . The system of  claim 11 , wherein the retail transaction records comprise points expiration data for each enrolled loyalty member. 
     
     
         16 . The system of  claim 11 , wherein the input parameters related to the retail loyalty program comprise the loyalty program for which a forecast is requested, the loyalty customer segment to be included in the forecast, and/or the forecast period. 
     
     
         17 . The system of  claim 11 , wherein the forecast parameters comprise a forecast of loyalty points to be gained by customers during a defined forecast period. 
     
     
         18 . The system of  claim 11 , wherein the forecast parameters comprise a forecast of loyalty points to be redeemed by customers during the defined forecast period during retail purchase transactions at point-of-sale systems. 
     
     
         19 . The system of  claim 11 , wherein the forecast parameters comprise a forecast of loyalty points adjustments to be made during the defined forecast period initiated by retailer personnel. 
     
     
         20 . The system of  claim 11 , wherein the forecast parameters comprise a forecast of the retailer's overall points liability at the end of the defined forecast period.